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Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction.

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    Date Created
    • 2016-06-28
    Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.1371/journal.pcbi.1004890
    • Identifier Type
      International standard serial number
      Identifier Value
      1553-734X
    • Identifier Type
      International standard serial number
      Identifier Value
      1553-7358

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    Noren, D. P., Long, B. L., Norel, R., Rrhissorrakrai, K., Hess, K., Hu, C. W., . . . Qutub, A. A. (2016). A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis. PLOS Computational Biology, 12(6). doi:10.1371/journal.pcbi.1004890

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